
from math import acos, sin
from typing import List, Tuple, Union

import numpy as np
import torch
from PIL import Image

from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel





class     __UpperCamelCase (							lowerCAmelCase__       ):






     """simple docstring"""







     lowerCAmelCase_							   =			['''vqvae''']



     def __init__(				self							:							Dict     ,						_A							:							AutoencoderKL     ,						_A							:							UNetaDConditionModel     ,						_A							:							Mel     ,						_A							:							Union[DDIMScheduler, DDPMScheduler]     ,						):







       """simple docstring"""




       super().__init__()
       self.register_modules(unet=_A     ,						scheduler=_A     ,						mel=_A     ,						vqvae=_A )



     def  UpperCAmelCase__							(				self							:							Optional[int] ):







       """simple docstring"""




       return 50 if isinstance(self.scheduler     ,						_A ) else 1000



     @torch.no_grad()
     def __call__(				self							:							Dict     ,						_A							:							int = 1     ,						_A							:							str = None     ,						_A							:							np.ndarray = None     ,						_A							:							int = 0     ,						_A							:							int = 0     ,						_A							:							int = None     ,						_A							:							torch.Generator = None     ,						_A							:							float = 0     ,						_A							:							float = 0     ,						_A							:							torch.Generator = None     ,						_A							:							float = 0     ,						_A							:							torch.Tensor = None     ,						_A							:							torch.Tensor = None     ,						_A							:							Union[str, Any]=True     ,						):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Any					=		steps or self.get_default_steps()
       self.scheduler.set_timesteps(_A )
       __SCREAMING_SNAKE_CASE      :	int					=		step_generator or generator
       # For backwards compatibility
       if type(self.unet.config.sample_size ) == int:
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		(self.unet.config.sample_size, self.unet.config.sample_size)
       if noise is None:
         __SCREAMING_SNAKE_CASE      :	int					=		randn_tensor(
             (
                 batch_size,
                 self.unet.config.in_channels,
                 self.unet.config.sample_size[0],
                 self.unet.config.sample_size[1],
             )     ,						generator=_A     ,						device=self.device     ,						)
       __SCREAMING_SNAKE_CASE      :	Any					=		noise
       __SCREAMING_SNAKE_CASE      :	List[str]					=		None

       if audio_file is not None or raw_audio is not None:
         self.mel.load_audio(_A     ,						_A )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.mel.audio_slice_to_image(_A )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		np.frombuffer(input_image.tobytes()     ,						dtype='''uint8''' ).reshape(
             (input_image.height, input_image.width) )
         __SCREAMING_SNAKE_CASE      :	List[str]					=		(input_image / 255) * 2 - 1
         __SCREAMING_SNAKE_CASE      :	str					=		torch.tensor(input_image[np.newaxis, :, :]     ,						dtype=torch.float ).to(self.device )

         if self.vqvae is not None:
           __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		self.vqvae.encode(torch.unsqueeze(_A     ,						0 ) ).latent_dist.sample(
               generator=_A )[0]
           __SCREAMING_SNAKE_CASE      :	Any					=		self.vqvae.config.scaling_factor * input_images

         if start_step > 0:
           __SCREAMING_SNAKE_CASE      :	Optional[int]					=		self.scheduler.add_noise(_A     ,						_A     ,						self.scheduler.timesteps[start_step - 1] )

         __SCREAMING_SNAKE_CASE      :	int					=		(
             self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
         )
         __SCREAMING_SNAKE_CASE      :	Tuple					=		int(mask_start_secs * pixels_per_second )
         __SCREAMING_SNAKE_CASE      :	List[Any]					=		int(mask_end_secs * pixels_per_second )
         __SCREAMING_SNAKE_CASE      :	str					=		self.scheduler.add_noise(_A     ,						_A     ,						torch.tensor(self.scheduler.timesteps[start_step:] ) )

       for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
         if isinstance(self.unet     ,						_A ):
           __SCREAMING_SNAKE_CASE      :	List[str]					=		self.unet(_A     ,						_A     ,						_A )['''sample''']
         else:
           __SCREAMING_SNAKE_CASE      :	List[Any]					=		self.unet(_A     ,						_A )['''sample''']

         if isinstance(self.scheduler     ,						_A ):
           __SCREAMING_SNAKE_CASE      :	int					=		self.scheduler.step(
               model_output=_A     ,						timestep=_A     ,						sample=_A     ,						eta=_A     ,						generator=_A     ,						)['''prev_sample''']
         else:
           __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.scheduler.step(
               model_output=_A     ,						timestep=_A     ,						sample=_A     ,						generator=_A     ,						)['''prev_sample''']

         if mask is not None:
           if mask_start > 0:
             __SCREAMING_SNAKE_CASE      :	Optional[int]					=		mask[:, step, :, :mask_start]
           if mask_end > 0:
             __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		mask[:, step, :, -mask_end:]

       if self.vqvae is not None:
         # 0.18215 was scaling factor used in training to ensure unit variance
         __SCREAMING_SNAKE_CASE      :	Optional[int]					=		1 / self.vqvae.config.scaling_factor * images
         __SCREAMING_SNAKE_CASE      :	Dict					=		self.vqvae.decode(_A )['''sample''']

       __SCREAMING_SNAKE_CASE      :	Optional[int]					=		(images / 2 + 0.5).clamp(0     ,						1 )
       __SCREAMING_SNAKE_CASE      :	int					=		images.cpu().permute(0     ,						2     ,						3     ,						1 ).numpy()
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		(images * 255).round().astype('''uint8''' )
       __SCREAMING_SNAKE_CASE      :	Dict					=		list(
           (Image.fromarray(_[:, :, 0] ) for _ in images)
           if images.shape[3] == 1
           else (Image.fromarray(_A     ,						mode='''RGB''' ).convert('''L''' ) for _ in images) )

       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		[self.mel.image_to_audio(_A ) for _ in images]
       if not return_dict:
         return images, (self.mel.get_sample_rate(), audios)

       return BaseOutput(**AudioPipelineOutput(np.array(_A )[:, np.newaxis, :] )     ,						**ImagePipelineOutput(_A ) )



     @torch.no_grad()
     def  UpperCAmelCase__							(				self							:							List[str]     ,						_A							:							List[Image.Image]     ,						_A							:							int = 50 ):







       """simple docstring"""




       assert isinstance(self.scheduler     ,						_A )
       self.scheduler.set_timesteps(_A )
       __SCREAMING_SNAKE_CASE      :	List[str]					=		np.array(
           [np.frombuffer(image.tobytes()     ,						dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
       __SCREAMING_SNAKE_CASE      :	Optional[Any]					=		(sample / 255) * 2 - 1
       __SCREAMING_SNAKE_CASE      :	List[Any]					=		torch.Tensor(_A ).to(self.device )

       for t in self.progress_bar(torch.flip(self.scheduler.timesteps     ,						(0,) ) ):
         __SCREAMING_SNAKE_CASE      :	int					=		t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		self.scheduler.alphas_cumprod[t]
         __SCREAMING_SNAKE_CASE      :	Any					=		(
             self.scheduler.alphas_cumprod[prev_timestep]
             if prev_timestep >= 0
             else self.scheduler.final_alpha_cumprod
         )
         __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		1 - alpha_prod_t
         __SCREAMING_SNAKE_CASE      :	Dict					=		self.unet(_A     ,						_A )['''sample''']
         __SCREAMING_SNAKE_CASE      :	str					=		(1 - alpha_prod_t_prev) ** 0.5 * model_output
         __SCREAMING_SNAKE_CASE      :	Dict					=		(sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
         __SCREAMING_SNAKE_CASE      :	Tuple					=		sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output

       return sample




     @staticmethod
     def  UpperCAmelCase__							(				_A							:							torch.Tensor     ,						_A							:							torch.Tensor     ,						_A							:							float ):







       """simple docstring"""




       __SCREAMING_SNAKE_CASE      :	Union[str, Any]					=		acos(torch.dot(torch.flatten(_A )     ,						torch.flatten(_A ) ) / torch.norm(_A ) / torch.norm(_A ) )
       return sin((1 - alpha) * theta ) * xa / sin(_A ) + sin(alpha * theta ) * xa / sin(_A )





'''simple docstring'''

import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_camembert import CamembertTokenizer
else:
			SCREAMING_SNAKE_CASE__     :						Optional[Any]	=  None


SCREAMING_SNAKE_CASE__     :						Tuple	=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE__     :						Union[str, Any]	=  {
    '''vocab_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_file''': {
        '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE__     :						List[Any]	=  {
    '''camembert-base''': 5_1_2,
}

SCREAMING_SNAKE_CASE__     :						Any	=  '''▁'''

class 							a__( snake_case__     ):
			a_	: Tuple									=      VOCAB_FILES_NAMES
			a_	: Optional[int]									=      PRETRAINED_VOCAB_FILES_MAP
			a_	: List[str]									=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a_	: str									=      ['''input_ids''', '''attention_mask''']
			a_	: Union[str, Any]									=      CamembertTokenizer






			def __init__(     self					,   _UpperCAmelCase=None					,   _UpperCAmelCase=None					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="</s>"					,   _UpperCAmelCase="<s>"					,   _UpperCAmelCase="<unk>"					,   _UpperCAmelCase="<pad>"					,   _UpperCAmelCase="<mask>"					,   _UpperCAmelCase=["<s>NOTUSED", "</s>NOTUSED"]					,   **_UpperCAmelCase					,   )							->							str:
					# Mask token behave like a normal word, i.e. include the space before it
					snake_case__	     =AddedToken(_UpperCAmelCase					,   lstrip=_UpperCAmelCase					,   rstrip=_UpperCAmelCase						) if isinstance(_UpperCAmelCase					,   _UpperCAmelCase						) else mask_token

					super().__init__(
					    _UpperCAmelCase					,   tokenizer_file=_UpperCAmelCase					,   bos_token=_UpperCAmelCase					,   eos_token=_UpperCAmelCase					,   sep_token=_UpperCAmelCase					,   cls_token=_UpperCAmelCase					,   unk_token=_UpperCAmelCase					,   pad_token=_UpperCAmelCase					,   mask_token=_UpperCAmelCase					,   additional_special_tokens=_UpperCAmelCase					,   **_UpperCAmelCase					,   )

					snake_case__	     =vocab_file
					snake_case__	     =False if not self.vocab_file else True






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:

					if token_ids_a is None:
							return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
					snake_case__	     =[self.cls_token_id]
					snake_case__	     =[self.sep_token_id]
					return cls + token_ids_a + sep + sep + token_ids_a + sep






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							List[int]:
					snake_case__	     =[self.sep_token_id]
					snake_case__	     =[self.cls_token_id]

					if token_ids_a is None:
							return len(cls + token_ids_a + sep						) * [0]
					return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






			def 						_lowercase  (     self					,   _UpperCAmelCase					,   _UpperCAmelCase = None						)							->							Tuple[str]:
					if not self.can_save_slow_tokenizer:
							raise ValueError(
							    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
							    'tokenizer.'						)

					if not os.path.isdir(_UpperCAmelCase						):
							logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""						)
							return
					snake_case__	     =os.path.join(
					    _UpperCAmelCase					,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']						)

					if os.path.abspath(self.vocab_file						) != os.path.abspath(_UpperCAmelCase						):
							copyfile(self.vocab_file					,   _UpperCAmelCase						)

					return (out_vocab_file,)




